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Designing treatment systems to accommodate process variability

page 153

16 DESIGNING TREATMENT SYSTEMS TO ACCOMMODATE
PROCESS VARIABILITY
Kenneth Chiang, Engineer
Douglas T. Merrill, Managing Engineer
Brown and Caldwell Consultants
Pleasant Hill, California 94523
Mary E. McLearn, Senior Project Manager
Electric Power Research Institute
Palo Alto, California 94303
INTRODUCTION
Effluent variability is one of the factors considered by industrial and municipal dischargers in
setting wastewater treatment facility design goals. This paper develops variability data associated with
the iron adsorption/coprecipitation process, also known as the iron treatment process. This process
can, under appropriate conditions, effectively reduce the concentration of certain metals found in
aqueous discharges. Treatment facility designers and regulatory agencies can use the guidelines presented in this paper to estimate effluent variability in the most likely to-be-used iron treatment process
configurations.
THE IRON TREATMENT PROCESS
The Electric Power Research Institute (EPRI) has sponsored a series of investigations designed to
develop costeffective trace metal removal technology. The investigations have focused upon the
adsorption/coprecipitation of trace metals with iron oxyhydroxide, an amorphous precipitate that
forms when a ferric salt (e.g., ferric chloride) is added to water.
FeCl3 + 3H20 - Fe(OH)3 i + 3C1" + 3H+ (1)
The trace metals (both dissolved and particulate) are adsorbed onto and trapped within the precipitate, which is then separated, leaving a purified effluent. Used on aqueous discharges, this process
effectively reduces the concentration of many priority metals including, arsenic, selenium, cadmium,
chromium, copper, lead, nickel, zinc, silver, antimony, and beryllium. Other wastewater impurities
(e.g., suspended solids) are also removed. Under appropriate conditions, the iron treatment process
can reduce dissolved and particulate metals concentrations to the part per billion (ppb) range. These
extremely low levels, which are near analytical detection limits, are now often required to meet the
limits set by regulatory agencies.
The equipment for the iron treatment process is the same that is used in conventional physical/
chemical water treatment systems. Figure 1 presents the block flow diagram for a typical iron treatment plant, which includes rapid mix and reaction tanks, a flocculation chamber, a clarifier, and an
optional filter. As indicated in the diagram, chemical feed systems and sludgehandling equipment are
also required.
EFFLUENT VARIABILITY, LONG-TERM AVERAGES, AND DISCHARGE LIMITS
Even in the most efficiently operated treatment plants, the measured effluent concentration of any
specific constituent varies continuously over time. This inherent variability is a result of fluctuations
in influent quality, changes in operating conditions, and variability in analytical precision. For
example, in a plot of copper concentration over time, as shown in Figure 2, the data yield a scattered
set of points. We can see that the data tend to cluster around the average concentration, in this case,
50 micrograms per liter (/ig/L). The average concentration of a specific constituent over a period of
46th Purdue Industrial Waste Conference Proceedings, 1992 Lewis Publishers, Inc., Chelsea, Michigan 48118.
Printed in U.S.A.
153

16 DESIGNING TREATMENT SYSTEMS TO ACCOMMODATE
PROCESS VARIABILITY
Kenneth Chiang, Engineer
Douglas T. Merrill, Managing Engineer
Brown and Caldwell Consultants
Pleasant Hill, California 94523
Mary E. McLearn, Senior Project Manager
Electric Power Research Institute
Palo Alto, California 94303
INTRODUCTION
Effluent variability is one of the factors considered by industrial and municipal dischargers in
setting wastewater treatment facility design goals. This paper develops variability data associated with
the iron adsorption/coprecipitation process, also known as the iron treatment process. This process
can, under appropriate conditions, effectively reduce the concentration of certain metals found in
aqueous discharges. Treatment facility designers and regulatory agencies can use the guidelines presented in this paper to estimate effluent variability in the most likely to-be-used iron treatment process
configurations.
THE IRON TREATMENT PROCESS
The Electric Power Research Institute (EPRI) has sponsored a series of investigations designed to
develop costeffective trace metal removal technology. The investigations have focused upon the
adsorption/coprecipitation of trace metals with iron oxyhydroxide, an amorphous precipitate that
forms when a ferric salt (e.g., ferric chloride) is added to water.
FeCl3 + 3H20 - Fe(OH)3 i + 3C1" + 3H+ (1)
The trace metals (both dissolved and particulate) are adsorbed onto and trapped within the precipitate, which is then separated, leaving a purified effluent. Used on aqueous discharges, this process
effectively reduces the concentration of many priority metals including, arsenic, selenium, cadmium,
chromium, copper, lead, nickel, zinc, silver, antimony, and beryllium. Other wastewater impurities
(e.g., suspended solids) are also removed. Under appropriate conditions, the iron treatment process
can reduce dissolved and particulate metals concentrations to the part per billion (ppb) range. These
extremely low levels, which are near analytical detection limits, are now often required to meet the
limits set by regulatory agencies.
The equipment for the iron treatment process is the same that is used in conventional physical/
chemical water treatment systems. Figure 1 presents the block flow diagram for a typical iron treatment plant, which includes rapid mix and reaction tanks, a flocculation chamber, a clarifier, and an
optional filter. As indicated in the diagram, chemical feed systems and sludgehandling equipment are
also required.
EFFLUENT VARIABILITY, LONG-TERM AVERAGES, AND DISCHARGE LIMITS
Even in the most efficiently operated treatment plants, the measured effluent concentration of any
specific constituent varies continuously over time. This inherent variability is a result of fluctuations
in influent quality, changes in operating conditions, and variability in analytical precision. For
example, in a plot of copper concentration over time, as shown in Figure 2, the data yield a scattered
set of points. We can see that the data tend to cluster around the average concentration, in this case,
50 micrograms per liter (/ig/L). The average concentration of a specific constituent over a period of
46th Purdue Industrial Waste Conference Proceedings, 1992 Lewis Publishers, Inc., Chelsea, Michigan 48118.
Printed in U.S.A.
153